Perioperative fatigue after lung resection for pulmonary nodules and early-stage lung cancer: a systematic review and meta-analysis
Original Article

Perioperative fatigue after lung resection for pulmonary nodules and early-stage lung cancer: a systematic review and meta-analysis

Shangqing Xu1,2 ORCID logo, Yan Sun1,3 ORCID logo, Kexin Ma4, Xiaoyan Han1 ORCID logo, Yu Gu4, Hongying Jin1 ORCID logo, Xiaoli Ma1,5,6 ORCID logo, Diego Gonzalez-Rivas1, Minjie Ma1,2,4,5,6,7,8 ORCID logo

1Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, China; 2Skills Training Center, The First Clinical Medical College of Lanzhou University, Lanzhou, China; 3The School of Nursing, Lanzhou University, Lanzhou, China; 4The First Clinical Medical College of Lanzhou University, Lanzhou, China; 5Research on the Application of Chest Artificial Intelligence and Intelligent Medical Integration in Gansu Province, Lanzhou, China; 6Gansu International Science and Technology Cooperation Base for Development and Application of Thoracic Surgery Key Technologies, Lanzhou, China; 7Department of Thoracic surgery, The Internet Hospital of the First Hospital of Lanzhou University, Lanzhou, China; 8Gansu Provincial Thoracic Surgery Medical Quality Control Center, Lanzhou, China

Contributions: (I) Conception and design: S Xu; (II) Administrative support: M Ma; (III) Provision of study materials or patients: All authors; (IV) Collection and assembly of data: Y Sun; (V) Data analysis and interpretation: H Jin; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Minjie Ma, MD. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, Lanzhou 730000, China; Skills Training Center, The First Clinical Medical College of Lanzhou University, Lanzhou, China; The First Clinical Medical College of Lanzhou University, Lanzhou, China; Research on the Application of Chest Artificial Intelligence and Intelligent Medical Integration in Gansu Province, Lanzhou, China; Gansu International Science and Technology Cooperation Base for Development and Application of Thoracic Surgery Key Technologies, Lanzhou, China; Department of Thoracic surgery, The Internet Hospital of the First Hospital of Lanzhou University, Lanzhou, China; Gansu Provincial Thoracic Surgery Medical Quality Control Center, Lanzhou, China. Email: maminjie24@sina.com.

Background: Fatigue is among the most common and function-limiting symptoms after lung resection, yet reported rates differ substantially because studies apply different instruments, thresholds, and assessment time points. This study aimed to synthesize the prevalence, time course, and correlations of postoperative fatigue following lung resection for pulmonary nodules/early-stage lung cancer.

Methods: We searched PubMed/PubMed Central (PMC) and screened reference lists of relevant reviews and included studies from inception to January 15, 2026 and screened reference lists. Observational studies and trials reporting postoperative fatigue after lung resection were eligible. The primary quantitative outcome was the prevalence of clinically significant (moderate-to-severe) fatigue defined by validated cutoffs on 0–10 symptom scales [typically ≥4/10, a widely accepted threshold for moderate-to-severe symptoms in oncology; Patient Subjective Assessment-Lung (PSA-Lung) fatigue >3]. We performed random-effects meta-analysis on the logit scale and conducted prespecified subgroup analyses by time window (perioperative ≤1 month vs. survivorship ≥1 year).

Results: Eleven studies (n=2,618 participants) met inclusion criteria; five studies provided extractable dichotomous data for clinically significant fatigue for meta-analysis. The pooled prevalence of clinically significant fatigue was 19.6% [95% confidence interval (CI): 11.6–31.3%; I2=78%] in the perioperative window and 17.7% (95% CI: 13.4–23.0%; I2=67%) in the survivorship window. Across all time points, the pooled prevalence was 18.6% (95% CI: 14.8–23.2%). Narrative synthesis of the remaining studies suggested that fatigue peaked in the first postoperative month and improved thereafter, but a substantial subgroup experienced persistent fatigue beyond 12 months. Study quality was appraised using the Newcastle-Ottawa Scale.

Conclusions: Available evidence suggests that approximately one in five patients may experience clinically significant fatigue after lung resection, and persistence beyond 1 year is common. Standardized measurement (instrument and cutoff), consistent assessment windows, and fatigue-specific perioperative pathways are needed. Interpretation is limited by the small number of studies with extractable dichotomous data and by heterogeneity.

Keywords: Perioperative fatigue; lung resection; early-stage lung cancer; thoracic surgery; meta-analysis

Submitted Feb 06, 2026. Accepted for publication Mar 31, 2026. Published online May 27, 2026.

doi: 10.21037/jtd-2026-1-0354

Highlight box

Key findings

• Approximately 20% of patients experience clinically significant moderate-to-severe fatigue after lung resection for pulmonary nodules or early-stage lung cancer, and this fatigue can persist for over a year in a substantial subgroup.

What is known and what is new?

• Postoperative fatigue is a common symptom following lung surgery that impairs quality of life, but reported prevalence rates vary widely due to heterogeneous measurement tools and time points.

• This systematic review and meta-analysis synthesizes evidence using validated cutoffs for clinically significant fatigue, revealing a pooled prevalence of approximately 19.6% in the perioperative window and 17.7% during long-term survivorship.

What is the implication, and what should change now?

• Routine, standardized screening for postoperative fatigue should be integrated into enhanced recovery after surgery pathways and long-term survivorship follow-up, alongside targeted multimodal interventions.

Introduction

Pulmonary nodule resection and surgery for early-stage lung cancer are increasingly delivered via minimally invasive techniques [including video-assisted thoracoscopic surgery (VATS) and robotic-assisted approaches] (1-8), but postoperative fatigue remains frequent and clinically consequential (9-16). This symptom can delay ambulation, impair pulmonary rehabilitation, and worsen quality of life (QoL) (17-24). Estimates vary widely because studies use different patient-reported outcome (PRO) instruments, different thresholds for clinical significance, and heterogeneous follow-up windows (25-32). A synthesis that prioritizes clinically significant fatigue and clarifies the postoperative time course is needed to inform perioperative care pathways (33-38). Therefore, this systematic review and meta-analysis aimed to synthesize the prevalence, time course, and clinical correlations of postoperative fatigue following lung resection for pulmonary nodules or early-stage lung cancer. We present this article in accordance with the PRISMA reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0354/rc) (39,40).

Methods

Protocol and reporting

Although eligibility criteria and analytic plan were determined a priori and no post hoc changes were made, the review was not prospectively registered, and no protocol was deposited.

Search strategy and eligibility: two reviewers (S.X. and Y.S.) independently screened titles/abstracts and full texts; disagreements were resolved by consensus or by consulting a third reviewer.

We searched PubMed/PubMed Central (PMC) and screened reference lists of relevant reviews and included studies. The database search included PubMed (n=120) and PubMed Central (PMC; n=57), and citation searching identified 2 additional records. Eligible studies enrolled adults undergoing lung resection for pulmonary nodules or early-stage lung cancer and reported postoperative fatigue using a validated instrument. We included both observational studies and randomized trials if fatigue was reported as an outcome. Non-surgical cohorts and studies without postoperative fatigue data were excluded (41-45). The detailed search strategy and study-level extractability matrix are provided in Appendix 1 and Table S1, respectively.

Outcomes

The primary quantitative outcome was clinically significant postoperative fatigue, defined using study-reported validated cutoffs on 0–10 symptom scales [typically ≥4/10, a widely accepted threshold for moderate-to-severe symptoms in oncology; Patient Subjective Assessment-Lung (PSA-Lung) fatigue >3]. Secondary outcomes included continuous fatigue severity trajectories and associated clinical factors (e.g., pain, dyspnea, sleep disturbance, inflammation) (5,16,46-50).

Data extraction and risk of bias

Two reviewers (K.M. and X.H.) extracted study characteristics, fatigue instruments, follow-up windows, and fatigue outcomes. Study quality was appraised using the Newcastle-Ottawa Scale (NOS) for observational studies and the Cochrane risk-of-bias approach for randomized trials, summarized descriptively (40,51-55).

Statistical analysis

For studies reporting dichotomous clinically significant fatigue, we conducted random-effects meta-analysis on the logit scale with DerSimonian-Laird τ2, and back-transformed pooled estimates to prevalence. The study-level dataset used for prevalence pooling (events and denominators by study and time window) is provided in Table S2. We additionally performed small-k sensitivity analyses using a Hartung-Knapp-Sidik-Jonkman adjustment and a restricted maximum likelihood (REML) τ2 estimator; results are shown in Table S3. Individual study 95% confidence intervals (CIs) were calculated using the Wilson score method. Heterogeneity was quantified using I2. Prespecified subgroup analyses compared perioperative (≤1 month) vs. survivorship (≥1 year) time windows (56-65). Because fewer than 10 studies contributed to any pooled estimate, we did not formally assess small-study effects or reporting/publication bias using funnel plots or regression-based tests. We did not perform a formal certainty-of-evidence assessment [e.g., Grading of Recommendations Assessment, Development and Evaluation (GRADE)].

Results

Study selection

The search identified 177 records from databases and 2 records from citation searching. After duplicate removal, 142 records were screened and 25 full texts were assessed. Fourteen full-text reports were excluded because they did not report postoperative fatigue. Eleven studies were included in qualitative synthesis, and five in quantitative synthesis (Figure 1).

Figure 1 PRISMA flow diagram.

Study characteristics

Included studies were conducted in Asia, Europe, and North America and encompassed thoracotomy and minimally invasive procedures. Fatigue was measured using multiple instruments, including the Brief Fatigue Inventory (BFI), Patient Subjective Assessment-Lung (PSA-Lung), EORTC QLQ-C30, SF-36 Vitality, and cancer-specific fatigue scales (e.g., Lee Fatigue Scale; Checklist Individual Strength). Table 1 summarizes study characteristics (14,18,50,66-73).

Table 1

Characteristics of included studies

Author, year Design Sample size (n) Follow-up Fatigue instrument Dichotomous fatigue data usable?
Huang et al. 2015 (PMC4468232) Cross-sectional (survivorship) 254 1–5 years post-surgery BFI (0–10) Yes (BFI ≥4: 57/254)
Sarna et al. 2008 (PMID 18776002) Prospective cohort 94 1 and 4 months Schwartz Cancer Fatigue Scale Partially (instrument-specific cutoffs; not pooled)
Hong et al. 2025 (TLCR; doi 10.21037/tlcr-24-702) Prospective cohort (comparative) 550 Pre-operative; POD 1–4; weekly up to 4 weeks PSA-Lung (includes fatigue); PROMIS-Fatigue (subset) Unclear (reported as trajectories; not pooled)
Haugøy et al. 2019 (BMJ Open; doi 10.1136/bmjopen-2018-028192) Prospective cohort 196 Baseline to 5 months Lee Fatigue Scale No (continuous only)
Kenny et al. 2008 (J Clin Oncol; doi 10.1200/JCO.2008.18.3999) Randomized trial (perioperative intervention) 152 4 weeks SF-36 Vitality (0–100) No (continuous only)
Hung et al. 2011 (J Pain Symptom Manage; doi 10.1016/j.jpainsymman.2010.04.019) Cross-sectional (survivorship) 350 1–5 years post-surgery BFI (0–10) Yes (BFI ≥4: 59/350)
Lowery et al. 2014 (Support Care Cancer; doi 10.1007/s00520-013-1968-3) Cross-sectional (survivorship) 183 1–6 years post-surgery BFI (0–10) Yes (BFI ≥4: ~25/183)
Liao et al. 2022 (J Cardiothorac Surg; doi 10.1186/s13019-022-01974-9) Cross-sectional at discharge 366 At discharge MDASI-LC (0–10) Yes (≥4: ~89/366)
Bendixen et al. 2016 (Lancet Oncol; doi 10.1016/S1470-2045(16)30075-2) Randomized trial (VATS vs. thoracotomy) 206 12 months SF-36 Vitality No (continuous only)
Lim et al. 2022 (VIOLET; NEJM Evid; doi: 10.1056/EVIDoa2100016) Randomized trial (VATS vs. open lobectomy) 503 Baseline; 5 weeks; 52 weeks EORTC QLQ-C30 (includes fatigue domain) No (continuous only; no extractable dichotomous fatigue counts)
Yang et al. 2022 (Curr Oncol; doi 10.3390/curroncol29100604) Prospective cohort 104 4 weeks PSA-Lung (fatigue item 0–10; categories 0–3 vs. >3) Yes (>3: 15/104)

BFI, Brief Fatigue Inventory; POD, post-operative day; PROMIS, Patient-Reported Outcomes Measurement Information System; PSA-Lung, Patient Subjective Assessment-Lung; SF-36, 36-item short-form health survey; VATS, video-assisted thoracoscopic surgery.

Clinically significant fatigue prevalence

Five studies reported extractable dichotomous data for clinically significant fatigue based on validated cutoffs on 0–10 scales. In perioperative assessments (discharge to 4 weeks), the pooled prevalence was 19.6% (95% CI: 11.6–31.3%; I2=78%). In survivorship assessments (≥1 year), the pooled prevalence was 17.7% (95% CI: 13.4–23.0%; I2=67%). Overall pooled prevalence across time points was 18.6% (95% CI: 14.8–23.2%; I2=71%) (Figure 2) (67,70-73). Given the small number of studies and high heterogeneity, these pooled estimates should be considered exploratory and hypothesis-generating. Sensitivity analyses using Hartung-Knapp-Sidik-Jonkman (HKSJ)/REML were directionally consistent (Table S3).

Figure 2 Forest plot of clinically significant postoperative fatigue prevalence (random-effects logit meta-analysis). CI, confidence interval.

Risk of bias

Overall study quality ranged from moderate to high (Figure 3). The main limitations were single-center sampling, limited adjustment for baseline fatigue and comorbidities, and variability in follow-up windows.

Figure 3 Study quality assessment using the NOS. NOS, Newcastle-Ottawa Scale.

Discussion

This review suggests that clinically significant fatigue affects roughly one in five patients after lung resection, with comparable pooled prevalence in the perioperative window and in longer-term survivorship. Many patients improve after the first postoperative month, but a meaningful subgroup continues to experience fatigue beyond 1 year. These findings argue for routine fatigue screening within enhanced recovery after surgery (ERAS) pathways and survivorship follow-up, ideally alongside structured assessment of co-occurring symptoms (pain, dyspnea, sleep disturbance, and emotional distress) (11,20-22,26,34-36,74-84). Longitudinal cohorts generally show an early postoperative peak followed by partial recovery over weeks; however, heterogeneous assessment schedules prevented a formal meta-analytic time-course model.

Substantial heterogeneity remains and likely reflects differences in surgical approach, patient case-mix, and fatigue measurement. Specifically, the included studies encompassed both open thoracotomy and minimally invasive techniques, which have distinct recovery trajectories. Furthermore, the use of different fatigue instruments with varying recall periods and scoring systems, along with diverse patient populations (e.g., varying stages of early lung cancer and benign nodules), contributed to the observed heterogeneity. Due to the limited number of studies providing extractable dichotomous data, we were unable to perform further stratification or meta-regression to isolate these factors. Standardization would materially improve interpretability: future studies should prespecify assessment windows (e.g., discharge, 30 days, 3 months, 12 months), adopt a core fatigue instrument (e.g., BFI or PROMIS Fatigue), and report both continuous scores and the proportion exceeding a clinically meaningful cutoff.

The diagnostic context may also influence fatigue burden. Pulmonary nodule cohorts often blend benign and malignant pathology, and the interval from nodule detection to definitive pathology can be psychologically taxing. Distress and anxiety during this period may amplify fatigue and related symptom clusters, over and above the physiologic impact of surgery. Future research should routinely report pathology (benign vs. malignant), stage, adjuvant therapy, and baseline psychological distress, and test whether these factors modify fatigue trajectories.

From a clinical standpoint, fatigue management will likely require a multimodal approach: optimized analgesia and sleep hygiene, early mobilization with pulmonary rehabilitation, nutritional support, and targeted interventions for patients at higher risk (e.g., older age, higher baseline symptom burden, or prolonged postoperative complications).

Limitations

First, our search strategy was limited to PubMed/PMC and reference lists; the omission of other databases such as Embase may have introduced a risk of missing relevant literature. Second, only five studies provided directly extractable dichotomous data for clinically significant fatigue, limiting precision and precluding robust meta-regression. Third, instruments and thresholds varied, and we harmonized definitions using instrument-specific cutoffs, which may not be equivalent across scales. Fourth, many studies did not measure baseline fatigue, making it difficult to separate new-onset postoperative fatigue from pre-existing fatigue, which is a critical confounder in oncology populations (60,61,85-87). An extractability matrix summarizing study-level reporting constraints is provided in Table S1. Finally, evidence from pulmonary nodule populations suggests that distress/anxiety is common at the time of nodule identification and in surgical ground-glass opacity (GGO) cohorts, and structured education/communication interventions can mitigate anxiety; these factors may contribute to fatigue burden but were rarely measured alongside fatigue in the included studies. When generic instruments are used (e.g., SF-36 Vitality), their construct validity as fatigue proxies should be considered, and studies should report clinically interpretable thresholds in addition to continuous scores (88-91).

Conclusions

Available evidence suggests that clinically significant fatigue is common after lung resection and may persist into survivorship. Harmonized PRO measurement and fatigue-focused perioperative and follow-up interventions are warranted.

Acknowledgments

All authors acknowledge the use of generative artificial intelligence (GAI) in the research and writing process. According to the GAIDeT taxonomy (2025), some tasks were delegated to GAI tools under full human supervision, including proofreading and editing and translation. The GAI tool used was: chatGPT5. Responsibility for the final manuscript lies entirely with the authors.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0354/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0354/prf

Funding: This study was supported by the Gansu Provincial Key Research and Development Program (No. 26YFFA004), Natural Science Foundation of Gansu Province (No. 23JRRA1597), the 2024 Key Research and Development Program of Gansu Province–International Cooperation Field (No. 24YFWA011), Gansu Provincial Health Department Research Program (No. GSWSKY2024-07), Gansu Provincial Joint Fund Project (No. 25JRRA1262), and the Institutional Research Fund of The First Hospital of Lanzhou University (No. ldyyyn2023-63).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0354/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article as: Xu S, Sun Y, Ma K, Han X, Gu Y, Jin H, Ma X, Gonzalez-Rivas D, Ma M. Perioperative fatigue after lung resection for pulmonary nodules and early-stage lung cancer: a systematic review and meta-analysis. J Thorac Dis 2026;18(5):518. doi: 10.21037/jtd-2026-1-0354

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